Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12124#discussion_r58811099
--- Diff: python/pyspark/ml/classification.py ---
@@ -1134,6 +1139,216 @@ def weights(self):
return self._call_java("weights")
+@inherit_doc
+class OneVsRest(Estimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
+ """
+ Reduction of Multiclass Classification to Binary Classification.
+ Performs reduction using one against all strategy.
+ For a multiclass classification with k classes, train k models (one
per class).
+ Each example is scored against all k models and the model with highest
score
+ is picked to label the example.
+
+ >>> from pyspark.sql import Row
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sc.parallelize([
+ ... Row(label=0.0, features=Vectors.dense(1.0, 0.8)),
+ ... Row(label=1.0, features=Vectors.sparse(2, [], [])),
+ ... Row(label=2.0, features=Vectors.dense(0.5, 0.5))]).toDF()
+ >>> lr = LogisticRegression(maxIter=5, regParam=0.01)
+ >>> ovr = OneVsRest(classifier=lr).setPredictionCol("indexed")
+ >>> model = ovr.fit(df)
+ >>> [x.coefficients for x in model.models]
+ [DenseVector([3.3925, 1.8785]), DenseVector([-4.3016, -6.3163]),
DenseVector([-4.5855, 6.1785])]
+ >>> [x.intercept for x in model.models]
+ [-3.6474708290602034, 2.5507881951814495, -1.1016513228162115]
+ >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0,
0.0))]).toDF()
+ >>> model.transform(test0).head().indexed
+ 1.0
+ >>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0],
[1.0]))]).toDF()
+ >>> model.transform(test1).head().indexed
+ 0.0
+ >>> test2 = sc.parallelize([Row(features=Vectors.dense(0.5,
0.4))]).toDF()
+ >>> model.transform(test2).head().indexed
+ 2.0
+
+ .. versionadded:: 2.0.0
+ """
+
+ # a placeholder to make it appear in the generated doc
+ classifier = Param(Params._dummy(), "classifier", "base binary
classifier")
+
+ @keyword_only
+ def __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ classifier=None):
+ """
+ __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
+ classifier=None)
+ """
+ super(OneVsRest, self).__init__()
+ kwargs = self.__init__._input_kwargs
+ self._set(**kwargs)
+
+ @keyword_only
+ @since("2.0.0")
+ def setParams(self, featuresCol=None, labelCol=None,
predictionCol=None, classifier=None):
+ """
+ setParams(self, featuresCol=None, labelCol=None,
predictionCol=None, classifier=None):
+ Sets params for OneVsRest.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ @since("2.0.0")
+ def setClassifier(self, value):
+ """
+ Sets the value of :py:attr:`classifier`.
+ """
+ self._paramMap[self.classifier] = value
+ return self
+
+ @since("2.0.0")
+ def getClassifier(self):
+ """
+ Gets the value of classifier or its default value.
+ """
+ return self.getOrDefault(self.classifier)
+
+ def _fit(self, dataset):
+ labelCol = self.getLabelCol()
+ featuresCol = self.getFeaturesCol()
+ predictionCol = self.getPredictionCol()
+ classifier = self.getClassifier()
+
+ numClasses = int(dataset.agg({labelCol:
"max"}).head()["max("+labelCol+")"]) + 1
+
+ multiclassLabeled = dataset.select(labelCol, featuresCol)
+
+ # persist if underlying dataset is not persistent.
+ handlePersistence = \
+ dataset.rdd.getStorageLevel() == StorageLevel(False, False,
False, False)
+ if handlePersistence:
+ multiclassLabeled.persist(StorageLevel.MEMORY_AND_DISK)
+
+ def trainSingleClass(index):
+ binaryLabelCol = "mc2b$" + str(index)
+ trainingDataset = multiclassLabeled.withColumn(
+ binaryLabelCol,
+ when(multiclassLabeled[labelCol] == float(index),
1.0).otherwise(0.0))
--- End diff --
But I'm hoping to fix trees to not need metadata for 2.0, if we have time.
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